A Few Shot Transfer Learning Approach Identifying Private Images with Fast User Personalization

Edoardo Serra, Sujeet Ayyapureddi, Qudrat E. Alahy Ratul, Anna C. Squicciarini

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

As online image sharing has become commonplace, researchers have acknowledged the need to assist users in detecting sensitive (or private) images. However, image privacy classification tasks have shown to be nontrivial, as the designation of an image sensitivity requires considerations of the visual concepts in the image. In this paper, we propose an innovative framework that combines the power of knowledge transfer for efficient, personalized learning of individuals’ privacy preferences toward images.

Our approach defines a meta-model, which, given the query image and a small set of labeled images (used for the user-privacy customization), identifies if the query image is private for a target user. A generic user can efficiently customize this model by providing a small labeled training set. Moreover, our proposed framework includes transfer learning techniques to import basic patterns for image processing learned from other domains. Transfer learning enables fast and accurate processing of images, and allows few shot learning to focus on customization. This helps speed up the training process and avoid risk of overfitting. Our proposed framework significantly outperforms several baselines, including advanced object-oriented approaches and other CNN-based methods.

Original languageAmerican English
Title of host publication2021 IEEE International Conference on Big Data (Big Data)
StatePublished - 1 Jan 2021

Keywords

  • privacy
  • sensitivity
  • social networking (online)
  • systematics
  • training
  • visualization

EGS Disciplines

  • Computer Sciences

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